In this chapter, Deep Memory with Particle Swarm Optimization (DMPSO) algorithm is presented, which is based on Particle Swarm Optimization initialized by the particles of Deep Memory Greedy Search (DMGS). The Particle Swarm Optimization (PSO) is a population based optimization technique, where the population is called a swarm. In PSO, each particle represents a possible solution to the optimiz...

In this paper a new Hybrid technique of Artificial Neural Network (ANN) and Vector Evaluated Particle Swarm Optimization (VEPSO) is presented as a forecasting strategy for day-ahead price of electricity market. The proposed technique the proposed intelligent technique is applied to weights and bias of ANN to improve the learning capability through the minimum error. A comprehensive comparative ...

In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles' diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this...

In this paper, we propose a PID parameter tuning of particle swarm optimization for multiobjective optimization characteristics of two regional power system PID controller design. By defining a comprehensive consideration of system output overshoot, rise time and the fitness function term steadystate error indicators, such as the ITAE, and in accordance with the performance requirements of the ...

Boundary conditions are often used in particle swarm optimization (PSO) in order to enhance the entire solution space of particles as far as possible. However, most of them are not categorized in detail, the boundary conditions used for comparisons are not in the same category and their performances vary in different engineering fields. In order to address these issues, this paper presents a co...

A new classifier algorithm based on Multilayer Perceptron Neural Network (MPNN), Apriori association rules, and Particle Swarm Optimization (PSO) models is proposed. It provides a comprehensive analytic method for establishing an Artificial Neural Network (ANN) with self-organizing architecture by finding an optimal number of hidden layers and their neurons, less number of effective features of...

for a uniform distribution of water, decrease in water waste and decrease in erosion of soil, it is important that a land be prepared with proper slopes along its length as well as width. the aim of leveling is to create appropriate slopes for irrigation and drainage on the lands that were not already properly levelled and of the same time creating the level surface with a minimum transport of ...

A recently proposed swarm intelligence technique for Fuzzy Cognitive Map learning is described. The technique employs the Particle Swarm Optimization algorithm to minimize a proper objective function, whose global minimizers correspond to suboptimal weight matrices of the Fuzzy Cognitive Map. New instances of an industrial test problem are studied, justifying the usefulness of the technique as ...

with the rapid development of the internet, the amount of information and data which are produced, are extremely massive. hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. data mining can overcome this problem. while data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. as the speed of ...

this article presents the application of two algorithms: heuristic big bang-big crunch (hbb-bc) and a heuristic particle swarm ant colony optimization (hpsaco) to discrete optimization of reinforced concrete planar frames subject to combinations of gravity and lateral loads based on aci 318-08 code. the objective function is the total cost of the frame which includes the cost of concrete, formw...